A comprehensive guide on Plotly

By Asif Rasool
November, 2024

What is Plotly?

Plotly is an open-source, interactive graphing library for creating high-quality visualizations in Python, R, JavaScript, and other programming languages. It's widely used for data science, machine learning, and business analytics due to its versatility and interactivity. Plotly is particularly known for producing dynamic charts, making it easier to visualize complex datasets and share insights with others.

Key Features:

• Interactive Visualizations: Pan, zoom, hover, and export data directly from the charts.
• Wide Range of Chart Types: Includes line charts, bar charts, scatter plots, 3D plots, pie charts, heatmaps, and geographic maps.
• Ease of Integration: Integrates with frameworks like Dash (for building analytical web applications) and works seamlessly with Jupyter Notebooks.
• Customizable: Allows detailed customization of layout, color, and annotations.
• Supports Big Data: Efficiently handles large datasets with WebGL-based plotting.

Common Use Cases:

• Data Analysis: Exploratory data analysis (EDA) and interactive dashboards.
• Business Intelligence: Reports and visual storytelling.
• Scientific Research: Complex, multi-dimensional data visualization.

Libraries:

• Plotly.py: Python implementation.
• Plotly R: R package.
• Dash: A Python framework by Plotly for building web applications with analytical data visualizations.

Installing Plotly and Cufflinks Libraries

!pip install plotly --quiet
!pip install cufflinks --quiet

Code Breakdown: !pip install plotly --quiet

Purpose: Installs the Plotly library. ! Symbol: Indicates that this command is executed in the system shell from within the notebook. --quiet Flag: Suppresses non-error messages, making the output cleaner. Why Plotly? Plotly is used to create interactive visualizations such as line charts, scatter plots, and geographic maps.

!pip install cufflinks --quiet

Purpose: Installs the Cufflinks library. --quiet: Suppresses verbose output for cleaner installation feedback. Why Cufflinks? Cufflinks bridges the gap between Pandas DataFrames and Plotly, allowing users to create Plotly charts directly from DataFrames using simple .iplot() commands

!pip install chart_studio --quiet
import pandas as pd
import numpy as np
import chart_studio.plotly as py  # Only necessary if using chart-studio uploads
import seaborn as sns
import plotly.express as px
import cufflinks as cf  # Added missing import for Cufflinks

%matplotlib inline

from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot  # Corrected 'ofline' to 'offline'
init_notebook_mode(connected=True)
cf.go_offline()  # This enables offline mode for Cufflinks/Plotly

This code installs and imports essential Python libraries for interactive data visualization and analysis. It prepares your environment, enabling offline Plotly and Cufflinks functionality, which is particularly useful when working with Jupyter Notebooks.

Basics

arr_1 = np.random.randn(50, 4)
df_1 = pd.DataFrame(arr_1, columns = ['A', 'B', 'C', 'D'])
df_1.head()
df_1.iplot()

Creating and Visualizing Data with NumPy, Pandas, and Cufflinks

This code snippet generates a random dataset, organizes it into a DataFrame, and creates an interactive plot using Cufflinks and Plotly.

---

Code Breakdown:

1. Generate a Random Array:

   arr_1 = np.random.randn(50, 4)
   

   • np.random.randn(50, 4):
     Creates a 2D NumPy array with 50 rows and 4 columns of random numbers drawn from a standard normal distribution (mean 0, standard deviation 1).
     
   • Output: An array similar to:

     [[ 0.56, -1.23,  0.44,  1.08],
      [-0.76,  0.78, -0.34,  0.23],
      ... 50 rows in total ...]
     

2. Create a Pandas DataFrame:

   df_1 = pd.DataFrame(arr_1, columns=['A', 'B', 'C', 'D'])
   

   • pd.DataFrame(arr_1):
     Converts the NumPy array into a Pandas DataFrame for easier manipulation and visualization.
   • columns=['A', 'B', 'C', 'D']:
     Names the four columns of the DataFrame as A, B, C, and D.
   • Output: The first few rows might look like:

             A          B          C          D
     0  0.56     -1.23       0.44      1.08
     1 -0.76      0.78      -0.34      0.23
     ... 50 rows in total ...
     

3. Display the First Five Rows:

   df_1.head()
   

   • Purpose: Displays the first five rows of the DataFrame for a quick inspection of the data.
   • Output: Shows columns A, B, C, and D with sample values.

4. Create an Interactive Plot:

   df_1.iplot()
   

   • iplot(): A Cufflinks method that creates an interactive Plotly plot from the DataFrame.
   • Default Plot: Generates a line chart with:
     • X-axis: Row index (0 to 49).
     • Y-axis: Values from columns A, B, C, and D.
   • Interactive Features: Hover to see data points, zoom, pan, and export the chart.

---

Line Plots

import plotly.graph_objects as go
df_stocks = px.data.stocks()
px.line(df_stocks, x='date', y ='GOOG', labels = {'x': 'Date', 'y': 'Price'})

px.line(df_stocks, x='date', y =['GOOG', 'AAPL'], 
        labels = {'x': 'Date', 'y': 'Price'}, title ='Apple vs. Google')

fig = go.Figure()
fig.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.AAPL,
                         mode = 'lines', name='Apple'))
fig.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.AMZN,
                         mode = 'lines+markers', name='Amazon'))
fig.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.GOOG,
                         mode = 'lines+markers', name='Google',
                        line=dict(color='firebrick', width =2,
                                 dash='dashdot')))

# fig.update_layout(title='Stock Price Data 2018 - 2020',
#                   xaxis_title='Price', yaxis_title='Date')
                  

fig.update_layout(
    xaxis=dict(
        showline=True, showgrid=False, showticklabels=True,
        linecolor='rgb(204, 204, 204)',
        linewidth=2, ticks='outside', tickfont=dict(
        family='Arial', size=12, color='rgb(82, 82, 82)'
        )),
    yaxis=dict(showgrid=False, zeroline=False, showline=False,
               showticklabels=False),
    autosize=False,
    margin=dict(
        autoexpand=False, l=100, r=20, t=110,),
    showlegend=False, plot_bgcolor='white')
        

Code Breakdown:

1. Import Libraries and Data:

   import plotly.graph_objects as go
   df_stocks = px.data.stocks()
   

   • import plotly.graph_objects as go: Imports the graph_objects module for detailed control over Plotly charts.
   • px.data.stocks(): Provides a built-in dataset containing stock prices for companies like Google (GOOG), Apple (AAPL), and Amazon (AMZN) over time.

2. Basic Line Chart Using Plotly Express:

   px.line(df_stocks, x='date', y='GOOG', labels={'x': 'Date', 'y': 'Price'})
   

   • px.line(): Creates a simple line chart.
   • x='date' and y='GOOG': Sets the X-axis to dates and the Y-axis to Google stock prices.
   • labels: Customizes the axis labels.

3. Comparing Multiple Stocks:

   px.line(df_stocks, x='date', y=['GOOG', 'AAPL'], 
           labels={'x': 'Date', 'y': 'Price'}, title='Apple vs. Google')
   

   • Multiple Y Values: Plots both Google (GOOG) and Apple (AAPL) stock prices on the same graph.
   • title: Adds a chart title, "Apple vs. Google".

4. Custom Plot Using Graph Objects:

   fig = go.Figure()
   

   • go.Figure(): Initializes an empty figure for adding traces (data series).

5. Adding Traces for Each Stock:

   fig.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.AAPL,
                            mode='lines', name='Apple'))
   

   • go.Scatter(): Defines a trace for a line plot.
     • x and y: Data for the X-axis (date) and Y-axis (AAPL stock price).
     • mode='lines': Displays the trace as a line plot.
     • name: Sets the legend label.

   Similar traces are added for Amazon (AMZN) and Google (GOOG):

   fig.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.AMZN,
                            mode='lines+markers', name='Amazon'))
   fig.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.GOOG,
                            mode='lines+markers', name='Google',
                            line=dict(color='firebrick', width=2, dash='dashdot')))
   

   • mode='lines+markers': Displays lines with markers at data points.
   • line Dictionary: Customizes the line style (e.g., color, width, and dash pattern).

6. Layout Customization:

   fig.update_layout(
       xaxis=dict(
           showline=True, showgrid=False, showticklabels=True,
           linecolor='rgb(204, 204, 204)', linewidth=2, ticks='outside',
           tickfont=dict(family='Arial', size=12, color='rgb(82, 82, 82)')
       ),
       yaxis=dict(showgrid=False, zeroline=False, showline=False,
                  showticklabels=False),
       autosize=False,
       margin=dict(autoexpand=False, l=100, r=20, t=110),
       showlegend=False,
       plot_bgcolor='white'
   )
   

   • X-Axis and Y-Axis Customization:
     • showline and showgrid: Control the visibility of axis lines and grid lines.
     • tickfont: Sets the font for axis ticks (e.g., family, size, color).
   • Layout Adjustments:
     • autosize and margin: Control the figure's size and margins.
     • showlegend=False: Hides the legend.
     • plot_bgcolor='white': Sets the background color to white.

Bar Charts

df_us = px.data.gapminder().query("country == 'United States'")
px.bar(df_us, x='year', y='pop')

df_tips = px.data.tips()
px.bar(df_tips, x= 'day', color='sex', title = 'Tips by Sex Each Day',
      labels={'tip': 'Tip Amount', 'day': 'Day of the week'})

This code creates two bar charts using Plotly Express. The first chart visualizes population data for the United States over time, while the second compares tips received by customer gender on different days.

---

1. U.S. Population Bar Chart:

df_us = px.data.gapminder().query("country == 'United States'")
px.bar(df_us, x='year', y='pop')

• px.data.gapminder():
  Loads the Gapminder dataset, which contains global data on life expectancy, GDP per capita, and population for various countries from 1952 to 2007.

• .query("country == 'United States'"):
  Filters the dataset to include only the data for the United States.

• px.bar(df_us, x='year', y='pop'):
  Creates a bar chart:

  • x='year': The X-axis displays the years (1952–2007).
  • y='pop': The Y-axis represents the population of the United States in each year.

---

2. Tips by Day and Gender Bar Chart:

df_tips = px.data.tips()
px.bar(df_tips, x='day', color='sex', title='Tips by Sex Each Day',
       labels={'tip': 'Tip Amount', 'day': 'Day of the week'})

• px.data.tips():
  Loads the Tips dataset, which contains data about restaurant bills, including:

  • Tip amounts
  • Total bill amounts
  • Customer gender
  • Day of the week
  • Smoking status

• px.bar(df_tips, x='day', color='sex', title='Tips by Sex Each Day', labels=...):
  Creates a bar chart:

  • x='day': The X-axis shows the days of the week (e.g., Thursday, Friday).
  • color='sex': Bars are color-coded based on customer gender (male or female).
  • title='Tips by Sex Each Day': Adds a title to the chart.
  • labels={'tip': 'Tip Amount', 'day': 'Day of the week'}: Customizes the axis labels:
    • 'tip' → 'Tip Amount': Clarifies that the values represent tips.
    • 'day' → 'Day of the week': Provides a more descriptive label for the days.

This bar chart visually compares the distribution of tips from male and female customers across different days, helping identify trends or patterns.

px.bar(df_tips, x='sex', y='total_bill', color='smoker', barmode='group')

Code Explanation:

This line of code creates a grouped bar chart using Plotly Express to compare total bill amounts by customer gender and smoking status.

---

Code Breakdown:

px.bar(df_tips, x='sex', y='total_bill', color='smoker', barmode='group')

• df_tips:
  Refers to the Tips dataset, which contains data about restaurant transactions, including information on:

  • Total bill amount (total_bill)
  • Customer gender (sex)
  • Smoking status (smoker)
  • Additional details like day, time, and tip amounts.

• px.bar():
  Generates a bar chart.

• x='sex':
  Sets the X-axis to represent the gender of the customers (Male or Female).

• y='total_bill':
  Sets the Y-axis to display the total bill amount for each customer.

• color='smoker':
  Colors the bars based on the smoking status of the customers (Yes or No), allowing for easy visual differentiation.

• barmode='group':
  Groups the bars side by side (instead of stacking them), making it easy to compare the total bill amounts for smokers and non-smokers within each gender.

---

df_europe = px.data.gapminder().query("continent == 'Europe' and year == 2007 and pop > 2.e6")
fig = px.bar(df_europe, x = 'country', y = 'pop', text ='pop', color = 'country')
fig

This code creates a bar chart using Plotly Express to visualize the population of European countries in the year 2007, filtering out smaller populations.

---

1. Load and Filter Data:

df_europe = px.data.gapminder().query("continent == 'Europe' and year == 2007 and pop > 2.e6")

• px.data.gapminder():
  Loads the Gapminder dataset, containing global data on GDP, life expectancy, and population across various countries and years.

• .query("continent == 'Europe' and year == 2007 and pop > 2.e6"):
  Filters the dataset with three conditions:

  • continent == 'Europe': Selects data only for countries in Europe.
  • year == 2007: Focuses on data from the year 2007.
  • pop > 2.e6: Includes only countries with a population greater than 2 million (2.e6 means 2 × 10^6).

---

2. Create the Bar Chart:

fig = px.bar(df_europe, x='country', y='pop', text='pop', color='country')

• px.bar():
  Generates a bar chart.

• x='country':
  Sets the X-axis to represent the countries in Europe.

• y='pop':
  Sets the Y-axis to represent the population of each country.

• text='pop':
  Displays the population values as labels on the bars.

• color='country':
  Colors each bar differently based on the country, enhancing visual distinction.

---

3. Display the Chart:

fig

• This line returns the figure object, rendering the bar chart in the output if used in a Jupyter Notebook or similar environment.

fig.update_traces(texttemplate='%{txt:.2s}', textposition='outside')
fig.update_layout(uniformtext_minsize=8)
fig.update_layout(xaxis_tickangle=-45)
fig

Code Explanation:

This code customizes a Plotly figure (fig) by updating trace text, setting minimum text size, and adjusting the X-axis labels. These modifications enhance the chart's readability and presentation.

---

Code Breakdown:

1. Update Trace Text Formatting:

   fig.update_traces(texttemplate='%{txt:.2s}', textposition='outside')
   

   • update_traces(): Applies updates to all traces (data series) in the figure.
   • texttemplate='%{txt:.2s}':
     Formats the text on the bars:
     • %{txt}: Refers to the text associated with each trace.
     • .2s: Limits the text to 2 significant figures, often used for simplifying large numbers.
   • textposition='outside':
     Displays the text outside the bars (above the bars for positive values).

---

2. Set Minimum Text Size:

   fig.update_layout(uniformtext_minsize=8)
   

   • update_layout(): Updates the figure's layout settings.
   • uniformtext_minsize=8:
     Ensures that all text labels have a minimum font size of 8 points, maintaining readability even if the default text size is smaller.

---

3. Rotate X-axis Labels:

   fig.update_layout(xaxis_tickangle=-45)
   

   • xaxis_tickangle=-45:
     Rotates the X-axis tick labels by -45 degrees. This helps:
     • Prevent overlap when labels are long.
     • Improve readability, especially when many labels are present.

---

4. Display the Figure:

   fig
   

   • This line renders the customized Plotly figure (fig) in the output, typically in a Jupyter Notebook or an interactive Python environment.

---

Scatter Plots

df_iris = px.data.iris()
px.scatter(df_iris, x='sepal_width', y ='sepal_length',
          color='species', size='petal_length',
          hover_data=['petal_width'])

Code Explanation:

This code uses Plotly Express to create an interactive scatter plot based on the Iris dataset. The chart visualizes relationships between different flower measurements, with color coding to distinguish between species.

---

Code Breakdown:

1. Load the Iris Dataset:

   df_iris = px.data.iris()
   

   • px.data.iris():
     Loads the built-in Iris dataset from Plotly. This dataset contains 150 samples of iris flowers with the following attributes:
     • Sepal Length
     • Sepal Width
     • Petal Length
     • Petal Width
     • Species (Setosa, Versicolor, Virginica)

2. Create a Scatter Plot:

   px.scatter(df_iris, x='sepal_width', y='sepal_length',
              color='species', size='petal_length',
              hover_data=['petal_width'])
   

   • px.scatter():
     Generates a scatter plot.

   • x='sepal_width':
     Sets the X-axis to display sepal width values.

   • y='sepal_length':
     Sets the Y-axis to display sepal length values.

   • color='species':
     Colors the points based on the species of the iris flower:

     • This helps differentiate between the three species visually.

   • size='petal_length':
     Adjusts the size of each point based on petal length.

     • Larger circles indicate longer petal lengths, adding another layer of information.

   • hover_data=['petal_width']:
     Displays petal width values when hovering over each data point.

     • Provides additional context without cluttering the chart.

---

fig = go.Figure()
fig.add_trace(go.Scatter(
    x=df_iris.sepal_width, y=df_iris.sepal_length,
    mode='markers',
    marker_color=df_iris.sepal_width,
    text=df_iris.species, marker=dict(showscale=True)
)
    )
fig.update_traces(marker_line_width=2, marker_size=10)

Code Explanation:

This code creates an interactive scatter plot using Plotly Graph Objects (go) to visualize relationships in the Iris dataset. It customizes marker size, color, and interactivity, resulting in a detailed, visually appealing plot.

---

Code Breakdown:

1. Create a Figure Object:

   fig = go.Figure()
   

   • go.Figure():
     Initializes an empty Figure object that will hold the scatter plot traces.

2. Add a Scatter Trace:

   fig.add_trace(go.Scatter(
       x=df_iris.sepal_width, y=df_iris.sepal_length,
       mode='markers',
       marker_color=df_iris.sepal_width,
       text=df_iris.species, marker=dict(showscale=True)
   ))
   

   • add_trace():
     Adds a data series (trace) to the figure.

   • go.Scatter():
     Defines a scatter plot trace.

   • x=df_iris.sepal_width, y=df_iris.sepal_length:
     Sets the X-axis to sepal width and the Y-axis to sepal length.

   • mode='markers':
     Displays data points as markers (dots), not lines.

   • marker_color=df_iris.sepal_width:
     Colors the markers based on the sepal width value.

     • Creates a color gradient that shows variations in sepal width.

   • text=df_iris.species:
     Displays the species name when hovering over each marker.

   • marker=dict(showscale=True):
     Shows a color scale (legend) on the plot, indicating the mapping between marker colors and sepal width values.

---

3. Customize Marker Appearance:

   fig.update_traces(marker_line_width=2, marker_size=10)
   

   • update_traces():
     Applies the following settings to all traces:
     • marker_line_width=2: Sets a 2-pixel border around each marker, making them more distinct.
     • marker_size=10: Sets the marker size to 10 pixels for better visibility.

---

fig = go.Figure(data=go.Scattergl(
    x = np.random.randn(100000),
    y = np.random.randn(100000),
    mode='markers',
    marker=dict(color=np.random.randn(100000),
                colorscale= 'Viridis',
                line_width=1)
    )
)
fig

Code Explanation:

This code creates a scatter plot using Plotly Graph Objects (go) with a large set of randomly generated data points. It utilizes Scattergl, which is optimized for plotting large datasets. The plot is configured with custom colors and marker styles, making it interactive and visually appealing.

---

Code Breakdown:

1. Create a Figure with Scattergl Trace:

   fig = go.Figure(data=go.Scattergl(
       x = np.random.randn(100000),
       y = np.random.randn(100000),
       mode='markers',
       marker=dict(color=np.random.randn(100000),
                   colorscale='Viridis',
                   line_width=1)
   ))
   

   • go.Figure(data=...):
     Creates a new Figure and specifies the data (in this case, a Scattergl trace).

   • go.Scattergl():
     The Scattergl trace is optimized for handling large datasets efficiently using WebGL, which provides faster rendering for large numbers of points.

     • The scatter plot will display points as markers.

2. Generate Random Data:

   • x = np.random.randn(100000):
     Generates 100,000 random values from a standard normal distribution for the X-axis.

   • y = np.random.randn(100000):
     Similarly, generates 100,000 random values from a standard normal distribution for the Y-axis.

3. Customize Marker Appearance:

   • mode='markers':
     The data points are displayed as markers (dots), not connected by lines.

   • marker=dict(...):
     Customizes the appearance of the markers:

     • color=np.random.randn(100000):
       Colors each marker based on random values from a standard normal distribution, creating a varied color for each marker.

     • colorscale='Viridis':
       Applies the Viridis color scale to the markers. This color scale is perceptually uniform, making it suitable for displaying data variation visually.

     • line_width=1:
       Sets the border line width around each marker to 1 pixel, making the points more distinct.

---

Pie Charts

df_asia = px.data.gapminder().query("year == 2007").query("continent =='Asia'")
px.pie(df_asia, values='pop', names='country', title ='Population of Asian Continent',
      color_discrete_sequence=px.colors.sequential.RdBu)

# For more color schemes: plotly.com/python/builtin-colorscales/

Code Explanation:

This code uses Plotly Express to create a pie chart that visualizes the population distribution of Asian countries in the year 2007. It applies a custom color scheme to enhance the visualization.

---

Code Breakdown:

1. Import Data from Plotly's Built-in Dataset:

   df_asia = px.data.gapminder().query("year == 2007").query("continent == 'Asia'")
   

   • px.data.gapminder():
     Loads the Gapminder dataset, which contains information about countries worldwide, including population, GDP, and life expectancy over various years.

   • .query("year == 2007"):
     Filters the dataset to include only records from the year 2007.

   • .query("continent == 'Asia'"):
     Further filters the dataset to include only countries in Asia.

   • Result:
     df_asia is a DataFrame containing population data for Asian countries in 2007.

2. Create a Pie Chart:

   px.pie(df_asia, values='pop', names='country', title='Population of Asian Continent',
          color_discrete_sequence=px.colors.sequential.RdBu)
   

   • px.pie():
     Generates a pie chart based on the specified DataFrame.

   • values='pop':
     Specifies that the population values ('pop') determine the size of each slice.

   • names='country':
     Each slice of the pie chart represents a country in Asia.

   • title='Population of Asian Continent':
     Sets the title of the chart.

   • color_discrete_sequence=px.colors.sequential.RdBu:
     Applies the 'RdBu' (Red-Blue) color scheme to the pie chart. This creates a visually appealing gradient where slices have distinct, contrasting colors.

---

Resulting Visualization:

• Pie Chart Display:

  • Each slice represents an Asian country, with its size proportional to the population.
  • Hovering over each slice shows the country name and its population.

• Color Gradient:

  • The RdBu color scheme uses a red-to-blue gradient, enhancing visual differentiation between countries.

---

Use Case:

This code effectively visualizes the population distribution across Asian countries in 2007, making it easy to compare the relative sizes of different populations. Such visualizations are useful for demographic analysis and understanding regional population dynamics.

---

Additional Resource:

• For more color schemes, you can explore the Plotly built-in color scales at:
  Plotly Color Scales

colors = ['blue', 'green', 'black', 'purple', 'red', 'brown']
fig = go.Figure(data=[go.Pie(labels=['Water', 'Grass', 'Normal','Psychic', 'Fire', 'Ground'],
                                    values=[110, 90, 80, 80, 70, 60])])
fig

Code Explanation:

This code creates a pie chart using Plotly Graph Objects (go), representing values corresponding to different categories. It assigns custom colors to each segment for better visual distinction.

---

Code Breakdown:

1. Define Custom Colors:

   colors = ['blue', 'green', 'black', 'purple', 'red', 'brown']
   

   • Creates a list named colors with six color names. Although these colors are defined, they are not applied directly in this code snippet but can be used later to customize the appearance of the pie chart segments.

2. Create a Pie Chart Figure:

   fig = go.Figure(data=[go.Pie(
       labels=['Water', 'Grass', 'Normal', 'Psychic', 'Fire', 'Ground'],
       values=[110, 90, 80, 80, 70, 60]
   )])
   

   • go.Figure():
     Initializes a new Figure object, which will hold the pie chart.

   • data=[go.Pie(...)]:
     Specifies that the data for the figure is a pie chart created with go.Pie().

   • labels=['Water', 'Grass', 'Normal', 'Psychic', 'Fire', 'Ground']:
     Defines the labels (categories) for each slice of the pie chart.

   • values=[110, 90, 80, 80, 70, 60]:
     Sets the values corresponding to each label. These values determine the size of each slice:

     • Water: 110
     • Grass: 90
     • Normal: 80
     • Psychic: 80
     • Fire: 70
     • Ground: 60

3. Display the Figure:

   fig
   

   • Renders the figure and displays the pie chart. In Jupyter Notebooks, this line will produce an interactive chart.

---

Resulting Visualization:

• Pie Chart Display:
  • Each segment represents one of the six categories with its size proportional to the value.
  • The labels (Water, Grass, etc.) are shown on each slice.
  • Hovering over each segment displays the label and its corresponding value.

---

Potential Customization:

Although the colors list is defined but unused in this code snippet, you can apply it to the pie chart using the marker attribute:

fig = go.Figure(data=[go.Pie(
    labels=['Water', 'Grass', 'Normal', 'Psychic', 'Fire', 'Ground'],
    values=[110, 90, 80, 80, 70, 60],
    marker=dict(colors=colors)  # Apply custom colors
)])

---

Use Case:

This code is useful for visualizing categorical data distributions. It's ideal for comparing proportions across different groups, such as:

• Market share of various products
• Population distributions by category
• Resource allocation across different segments

fig.update_traces(hoverinfo='label+percent', textfont_size=12, textinfo='label+percent',
                 pull=[0.1,0, 0.2, 0, 0, 0],
                 marker=dict(colors=colors, line=dict(color='#FFFFFF', width=2)))
fig

Code Explanation:

This code snippet customizes the appearance and behavior of a Pie Chart using Plotly by updating its traces (data series). It enhances the chart's interactivity, style, and visual appeal.

---

Code Breakdown:

1. Update Traces:

   fig.update_traces(
       hoverinfo='label+percent',
       textfont_size=12,
       textinfo='label+percent',
       pull=[0.1, 0, 0.2, 0, 0, 0],
       marker=dict(colors=colors, line=dict(color='#FFFFFF', width=2))
   )
   

   • fig.update_traces(): Updates all traces (data series) in the figure. In this context, the pie chart is modified.

2. hoverinfo='label+percent':

   • Determines what information is displayed when hovering over a pie slice:
     • label: Displays the slice's label (e.g., 'Water').
     • percent: Shows the percentage value of the slice relative to the total.

3. textfont_size=12:

   • Sets the font size of the text inside the pie chart slices to 12 points.

4. textinfo='label+percent':

   • Specifies what text appears inside the pie slices:
     • label: Shows the category name on each slice.
     • percent: Displays the percentage of the total value for each slice.

5. pull=[0.1, 0, 0.2, 0, 0, 0]:

   • Creates a "pull-out" effect for specific slices by separating them from the center:
     • The first slice (Water) is pulled out by 10% of the pie's radius.
     • The third slice (Normal) is pulled out by 20%.
     • Other slices remain in their default positions.

6. marker=dict(...):

   • Customizes the appearance of the pie slices.
   • colors=colors: Applies the predefined color list to the slices.
   • line=dict(color='#FFFFFF', width=2): Adds a white border around each slice with a 2-pixel width. This enhances visual separation between segments.

---

Resulting Visualization Enhancements:

• Interactive Hover Information:
  Displaying both the label and percentage when hovering over slices provides richer insights.
• Text Display:
  Labels and percentages inside slices make the chart easy to interpret without external legends.
• Pull-out Effect:
  Highlights specific slices (Water and Normal), drawing attention to important categories.
• Visual Borders:
  White borders improve slice distinction, especially when adjacent colors are similar.

---

Use Case:

This enhanced pie chart can be useful for:

• Emphasizing specific segments: Highlighting key categories in reports.
• Presenting clear, concise information: Improved readability for presentations or dashboards.
• Creating visually appealing charts: Better aesthetics for data storytelling.

Histogram

dice_1 = np.random.randint(1, 7, 5000)
dice_2 = np.random.randint(1, 7, 5000)
dice_sum = dice_1 + dice_2
fig = px.histogram(dice_sum, nbins=11,
                  labels={'values': 'Dice Roll'},
                  title='5000 Dice Roll Histogram',
                  marginal = 'violin',
                  color_discrete_sequence=['green'])
fig

Code Explanation:

This code simulates rolling two six-sided dice 5,000 times and visualizes the distribution of the sums using a histogram with Plotly Express.

---

Code Breakdown:

1. Simulating Dice Rolls:

   dice_1 = np.random.randint(1, 7, 5000)
   dice_2 = np.random.randint(1, 7, 5000)
   

   • np.random.randint(1, 7, 5000): Generates an array of 5,000 random integers between 1 and 6 (inclusive), simulating the outcome of rolling a standard six-sided die.
   • dice_1 and dice_2 each represent the result of rolling one die 5,000 times.

2. Summing Dice Rolls:

   dice_sum = dice_1 + dice_2
   

   • Adds the results of the two dice for each roll, creating an array of 5,000 sums.
   • Possible sums range from 2 (1+1) to 12 (6+6).

3. Creating the Histogram:

   fig = px.histogram(dice_sum, nbins=11,
                      labels={'values': 'Dice Roll'},
                      title='5000 Dice Roll Histogram',
                      marginal='violin',
                      color_discrete_sequence=['green'])
   

   • px.histogram(): Generates a histogram to visualize the distribution of dice sums.
   • dice_sum: Data passed to the histogram for plotting.
   • nbins=11: Sets the number of bins to 11 (for sums ranging from 2 to 12).
   • labels={'values': 'Dice Roll'}: Customizes the axis label.
   • title='5000 Dice Roll Histogram': Sets the chart title.
   • marginal='violin': Adds a violin plot on the side of the histogram, showing the density distribution of the dice sums.
   • color_discrete_sequence=['green']: Colors the histogram bars green.

4. Display the Chart:

   fig
   

   • Renders the plotly histogram in an interactive environment (such as a Jupyter Notebook).

---

Resulting Visualization Insights:

• Dice Sum Distribution:
  The histogram will show a bell-shaped curve, peaking at 7. This reflects that:

  • There are more combinations to get 7 (e.g., 1+6, 2+5, 3+4, etc.) than any other sum.
  • Sums near 2 and 12 are less frequent because fewer combinations produce them.

• Violin Plot:
  The marginal violin plot adds a smoothed representation of the data distribution, making it easier to see where values cluster most.

---

Use Case:

This simulation and visualization are valuable for:

• Teaching probability concepts: Understanding why certain outcomes are more likely when rolling two dice.
• Statistical simulations: Demonstrating expected distributions from random experiments.
• Data analysis practice: Applying Python's statistical and visualization libraries to real-world scenarios.

fig.update_layout(
    xaxis_title_text = 'Dice Roll',
    yaxis_title_text = 'Dice Sum',
    bargap=0.2, showlegend=False
)

fig

Code Explanation:

This code customizes the layout of a Plotly histogram chart by modifying axis labels, adjusting the bar gap, and hiding the legend.

---

Code Breakdown:

1. Updating Layout Configuration:

   fig.update_layout(
       xaxis_title_text='Dice Roll',
       yaxis_title_text='Dice Sum',
       bargap=0.2,
       showlegend=False
   )
   

   • xaxis_title_text='Dice Roll': Sets the label for the x-axis to "Dice Roll." This indicates that the x-axis represents the sum of two dice rolls.
   • yaxis_title_text='Dice Sum': Sets the label for the y-axis to "Dice Sum." This could be more appropriately worded as "Frequency," indicating the count of occurrences for each sum.
   • bargap=0.2: Adjusts the gap between the bars in the histogram to 20% of each bar's width. This makes the chart visually clearer and more aesthetically pleasing.
   • showlegend=False: Hides the legend. Since this plot has only one data series (sum of dice rolls), a legend is unnecessary.

2. Display the Chart:

   fig
   

   • Renders the updated chart with the new layout settings.

---

Resulting Changes:

• Axis Labels: The chart will now display more informative axis titles, enhancing readability.
• Bar Gap: The slight gap between bars helps distinguish individual bars better.
• Legend Removed: Simplifies the chart when no legend is needed, reducing visual clutter.

---

Use Case:

These layout customizations are useful for:

• Improving chart clarity and accessibility.
• Ensuring the visual representation aligns with the audience's understanding of the data.
• Fine-tuning aesthetics for presentations or reports.

fig.update_layout(
    xaxis_title_text = 'Dice Roll',
    yaxis_title_text = 'Dice Sum',
    bargap=0.2, showlegend=False
)

df_tips= px.data.tips()
px.histogram(df_tips, x='total_bill', color='sex')

Code Explanation:

This code has two distinct parts: the first updates the layout of an existing Plotly figure, while the second creates a new histogram using the tips dataset. Let's break down each part separately.

---

1. Customizing the Layout of a Dice Roll Histogram:

fig.update_layout(
    xaxis_title_text='Dice Roll',
    yaxis_title_text='Dice Sum',
    bargap=0.2,
    showlegend=False
)

This part customizes the layout of a previously defined fig object (likely a histogram or bar chart representing dice rolls).

• xaxis_title_text='Dice Roll': Sets the label for the x-axis to "Dice Roll," indicating the values rolled with the dice.
• yaxis_title_text='Dice Sum': Sets the label for the y-axis to "Dice Sum," though "Frequency" might be more appropriate for a histogram.
• bargap=0.2: Creates a 20% gap between bars, making individual bars more distinct.
• showlegend=False: Hides the legend, which may be unnecessary for a single dataset.

---

2. Creating a Histogram with the Tips Dataset:

df_tips = px.data.tips()
px.histogram(df_tips, x='total_bill', color='sex')

This part creates a new histogram using the Plotly Express library and the built-in tips dataset.

• df_tips = px.data.tips(): Loads the tips dataset, which contains information about restaurant bills, including:

  • Total bill amounts.
  • Tip amounts.
  • Customer gender (sex).
  • Day of the week.
  • Time of day.
  • Number of guests at each table.

• px.histogram(df_tips, x='total_bill', color='sex'): Generates a histogram to visualize the distribution of total bill amounts, with bars colored by the sex of the customer.

  • x='total_bill': Specifies that the x-axis represents the total bill amounts.
  • color='sex': Colors the bars based on the gender of the customer, allowing comparison of spending patterns between male and female customers.

---

Combined Impact:

• The first section customizes a histogram layout, improving its clarity and visual appeal.
• The second section creates an entirely new histogram, providing insights into the distribution of restaurant bills and how they vary by customer gender. Both code snippets demonstrate essential data visualization techniques using Plotly.

Box Plots

df_tips = px.data.tips()
px.box(df_tips, x='sex', y='tip', points='all')

Explanation of the Code:

This code generates a boxplot using Plotly Express to visualize the distribution of tip amounts for different genders in the tips dataset.

---

Code Breakdown:

1. Load Data:

   df_tips = px.data.tips()
   

   • Loads the built-in tips dataset, which contains information about restaurant bills, including total bill amounts, tips, gender, day of the week, and more.

2. Create Boxplot:

   px.box(df_tips, x='sex', y='tip', points='all')
   

   • x='sex': Groups the data by the sex column, creating a separate boxplot for each gender.
   • y='tip': Plots the tip column on the y-axis, showing the distribution of tip amounts.
   • points='all': Displays all individual data points on the boxplot, adding transparency and detail to the visualization.

---

Key Features of the Boxplot:

• Box and Whiskers: Show the median, quartiles, and potential outliers in the tip data for each gender.
• Individual Data Points: Help visualize the spread and density of the data.
• Comparison: Easily compare the distribution of tips between male and female customers.

---

Result:

The output will display a boxplot comparing the tip amounts for male and female customers, with data points overlaid for better insight into individual values. This helps in understanding if one gender tends to give higher or more consistent tips.

px.box(df_tips, x='day', y='tip', color='sex')

Explanation of the Code:

This code creates a grouped boxplot using Plotly Express to visualize the distribution of tip amounts across different days of the week, separated by customer gender.

---

Code Breakdown:

1. Load Data:

   df_tips = px.data.tips()
   

   • Loads the built-in tips dataset, which contains details about restaurant transactions, including the day of the week, tip amount, total bill, gender, and more.

2. Create Boxplot:

   px.box(df_tips, x='day', y='tip', color='sex')
   

   • x='day': Groups the data by the day column, creating a separate boxplot for each day of the week.
   • y='tip': Plots the tip column on the y-axis, showing the distribution of tip amounts.
   • color='sex': Differentiates the boxplots by the sex column, showing separate boxes for male and female customers within each day.

---

Key Features of the Boxplot:

• Box and Whiskers: Represent the median, quartiles, and potential outliers of the tip data for each day and gender.
• Color Differentiation: Uses different colors to distinguish between male and female customers, allowing for easy comparison within each day.
• Distribution Insight: Helps identify patterns, such as which day tends to have higher tips or whether tipping behavior differs between genders on specific days.

---

Result:

The output will show a boxplot for each day (Sunday through Saturday), with two boxplots per day (one for male customers and one for female customers). This allows you to compare tip distributions across days and genders, providing insights into tipping trends and variations.

px.box(df_tips, x='day', y='tip', color='sex')
fig =  go.Figure()
fig.add_trace(go.Box(x=df_tips.sex, y=df_tips.tip, 
                     marker_color='blue', boxmean='sd'))

Explanation of the Code:

This code generates two different boxplots using Plotly Express and Plotly Graph Objects, both visualizing tip amounts from the tips dataset. Let's break it down step-by-step.

---

Code Breakdown:

1. Load Data:

   df_tips = px.data.tips()
   

   • This loads the built-in tips dataset, which includes information about restaurant bills, tips, customer gender, and more.

2. Create Boxplot with Plotly Express:

   px.box(df_tips, x='day', y='tip', color='sex')
   

   • x='day': Groups the data by the day column, creating a separate boxplot for each day of the week.
   • y='tip': Plots the tip amounts on the y-axis.
   • color='sex': Colors the boxplots based on the sex column, creating separate boxes for male and female customers for each day.
   • Result: This produces a grouped boxplot, showing tip distributions by gender for each day.

3. Create Boxplot with Graph Objects:

   fig = go.Figure()
   fig.add_trace(go.Box(x=df_tips.sex, y=df_tips.tip, 
                        marker_color='blue', boxmean='sd'))
   

   • go.Figure(): Initializes a new empty figure object.
   • go.Box: Adds a boxplot to the figure:
     • x=df_tips.sex: Groups the data by the sex column (creating separate boxes for male and female customers).
     • y=df_tips.tip: Displays the tip amounts on the y-axis.
     • marker_color='blue': Sets the color of the boxplot to blue.
     • boxmean='sd': Displays the mean and standard deviation of the data within each boxplot.
   • Result: This creates a simple boxplot comparing the distribution of tips between male and female customers, with the mean and standard deviation highlighted.

---

Key Differences:

1. Plotly Express (px.box):

   • Generates more feature-rich, straightforward visualizations with minimal code.
   • Supports automatic legends and multiple color groupings.
   • Easier to use for quick data exploration.

2. Graph Objects (go.Box):

   • Provides finer control over the plot’s appearance and behavior.
   • Useful when building custom visualizations or combining multiple traces.
   • Requires more code but offers greater customization options.

---

Overall:

• The first boxplot (px.box) provides a quick comparison of tips by day and gender.
• The second boxplot (go.Box) focuses on a detailed comparison of tips by gender, with additional information on the mean and standard deviation.

df_stocks = px.data.stocks()
fig =  go.Figure()
fig.add_trace(go.Box(y=df_stocks.GOOG, boxpoints='all', 
                     fillcolor='blue', jitter=0.5,
                    whiskerwidth=0.2))

fig.add_trace(go.Box(y=df_stocks.AAPL, boxpoints='all', 
                     fillcolor='red', jitter=0.5,
                    whiskerwidth=0.2))

fig.update_layout(title='Google Vs. Apple',
                 yaxis=dict(gridcolor='rgb(255, 255, 255)',
                           gridwidth=3),
                 paper_bgcolor ='rgb(243, 243, 243)',
                 plot_bgcolor ='rgb(243, 243, 243)')

Explanation of the Code:

This code generates a comparative boxplot for Google and Apple stock prices using Plotly's Graph Objects library. Let's break it down step by step:

---

Code Breakdown:

1. Load Stock Data:

   df_stocks = px.data.stocks()
   

   • This loads the built-in stocks dataset from Plotly Express, containing stock price data for various companies (like Google, Apple, and Amazon) over a period of time.
   • Columns include: date, GOOG (Google), AAPL (Apple), AMZN (Amazon), etc.

2. Initialize a Figure:

   fig = go.Figure()
   

   • Creates an empty Figure object, which will hold the boxplot traces.

3. Add Google Stock Boxplot:

   fig.add_trace(go.Box(y=df_stocks.GOOG, boxpoints='all',
                        fillcolor='blue', jitter=0.5,
                        whiskerwidth=0.2))
   

   • y=df_stocks.GOOG: Sets the y values to Google stock prices.
   • boxpoints='all': Displays all data points (outliers and inliers) on the boxplot.
   • fillcolor='blue': Fills the boxplot with a blue color.
   • jitter=0.5: Adds slight random noise (jitter) to the data points to reduce overlap and improve visibility.
   • whiskerwidth=0.2: Specifies the width of the whiskers (lines extending from the box).

4. Add Apple Stock Boxplot:

   fig.add_trace(go.Box(y=df_stocks.AAPL, boxpoints='all',
                        fillcolor='red', jitter=0.5,
                        whiskerwidth=0.2))
   

   • Similar to the Google boxplot but uses Apple (df_stocks.AAPL) data.
   • fillcolor='red': Colors the Apple boxplot in red.

5. Customize Layout:

   fig.update_layout(
       title='Google Vs. Apple',
       yaxis=dict(
           gridcolor='rgb(255, 255, 255)',  # White gridlines
           gridwidth=3                     # Gridline thickness
       ),
       paper_bgcolor='rgb(243, 243, 243)',  # Light gray background for the figure
       plot_bgcolor='rgb(243, 243, 243)'    # Light gray background for the plot area
   )
   

   • title='Google Vs. Apple': Sets the main title of the plot.
   • yaxis: Customizes the y-axis grid:
     • gridcolor: Sets gridlines to white.
     • gridwidth: Makes gridlines thicker for better visibility.
   • Background Colors:
     • paper_bgcolor: Sets the overall background color outside the plot area.
     • plot_bgcolor: Sets the background color within the plot area.

---

What This Code Achieves:

• Boxplot Visualization: Shows the distribution of Google and Apple stock prices, including median, quartiles, and outliers.
• Color Distinction: Blue and red boxes make it easy to differentiate between the two stocks.
• Enhanced Readability: Custom gridlines and background colors improve visual clarity.

This visualization helps compare the variability and central tendencies of Google and Apple stock prices in a clear and informative way.

Violin Plots

df_tips = px.data.tips()
px.violin(df_tips, y='total_bill', box=True, points='all') 

Explanation of the Code:

This code uses Plotly Express to create a violin plot for visualizing the distribution of total bill amounts in the tips dataset. Let's break it down:

---

Code Breakdown:

1. Load the Tips Dataset:

   df_tips = px.data.tips()
   

   • px.data.tips(): Loads Plotly's built-in tips dataset, which contains information about restaurant bills, including:
     • total_bill: The total amount of the bill.
     • tip: The tip amount.
     • sex: Gender of the customer.
     • smoker: Whether the customer smoked or not.
     • day: Day of the week.
     • time: Meal time (Lunch or Dinner).
     • size: Number of people at the table.

2. Create a Violin Plot:

   px.violin(df_tips, y='total_bill', box=True, points='all')
   

   • px.violin(): Creates a violin plot, which shows the distribution of numerical data and its probability density.
     • y='total_bill': Plots total_bill values along the y-axis.
     • box=True: Adds an embedded boxplot inside the violin plot to show quartiles (minimum, first quartile, median, third quartile, and maximum).
     • points='all': Displays all data points on the plot, providing a detailed view of individual values.

---

What the Violin Plot Shows:

• Distribution Shape: The width of the violin at different y-values indicates the density of data points. Wider sections represent higher data concentration.
• Central Tendency: The boxplot within the violin shows the median (center line) and interquartile range (box edges).
• Outliers and Spread: Data points outside the boxplot whiskers highlight potential outliers or extreme values.

---

Use Case:

This visualization helps understand:

• How total_bill values are distributed.
• The central tendency and variability of bill amounts.
• Outliers and overall spread in the dataset.

px.violin(df_tips, y='total_bill', box=True, points='all') 
px.violin(df_tips, y='tip', x='smoker', color = 'sex', box=True, points='all',
         hover_data= df_tips.columns) 

Explanation of the Code:

This code uses Plotly Express to create two violin plots from the tips dataset, each providing different insights into the data. Let's break it down step by step:

---

1. First Violin Plot:

px.violin(df_tips, y='total_bill', box=True, points='all')

Description:

• Violin Plot for total_bill:
  • y='total_bill': The plot shows the distribution of total_bill values on the y-axis.
  • box=True: Embeds a boxplot within the violin plot to display statistical summaries (minimum, median, and maximum).
  • points='all': Displays all individual data points, helping visualize the data spread and identify potential outliers.

Purpose:
This plot provides an overview of the distribution of bill amounts, including key statistics and variability.

---

2. Second Violin Plot:

px.violin(df_tips, y='tip', x='smoker', color='sex', box=True, points='all',
          hover_data=df_tips.columns)

Description:

• y='tip': Shows the distribution of tip values on the y-axis.
• x='smoker': Separates the plot into two groups: smokers and non-smokers, displayed along the x-axis.
• color='sex': Uses different colors to distinguish between male and female customers.
• box=True: Includes a boxplot within each violin to show key summary statistics.
• points='all': Plots all individual data points for a detailed view.
• hover_data=df_tips.columns: Displays additional information about each data point when hovered over. This includes all columns in the tips dataset (such as total_bill, day, and time).

Purpose:
This plot enables a detailed comparison of tip distributions between smokers and non-smokers, broken down by gender. It reveals patterns such as:

• Differences in tipping behavior between males and females.
• Whether smoking status influences tip amounts.

---

Key Insights You Can Gain:

1. Distribution Analysis:

   • Identify how tip amounts vary across different groups.
   • Spot potential outliers or inconsistencies in tipping behavior.

2. Comparison Between Groups:

   • Understand if smoking status affects tipping patterns.
   • Compare male and female customers' tipping habits within each group.

---

Conclusion:

These violin plots provide a rich, detailed view of the tips dataset, helping identify patterns, variations, and insights into customer behavior in relation to bill amounts and tipping.

fig = go.Figure()
fig.add_trace(go.Violin(x=df_tips['day'][df_tips['smoker']=='Yes'],
                       y=df_tips['total_bill'][df_tips['smoker']=='Yes'],
                       legendgroup='Yes', scalegroup='Yes', name='Yes',
                       side='negative', line_color='blue'))

fig.add_trace(go.Violin(x=df_tips['day'][df_tips['smoker']=='No'],
                       y=df_tips['total_bill'][df_tips['smoker']=='No'],
                       legendgroup='Yes', scalegroup='Yes', name='No',
                       side='positive', line_color='red'))

Explanation of the Code:

This code uses Plotly's Graph Objects (go) to create a split violin plot for the total_bill data in the tips dataset. The plot compares smokers and non-smokers across different days.

---

Step-by-Step Breakdown:

1. Figure Initialization:

   fig = go.Figure()
   

   • Initializes an empty figure object named fig.

---

2. First Violin Trace (Smokers):

   fig.add_trace(go.Violin(
       x=df_tips['day'][df_tips['smoker'] == 'Yes'],
       y=df_tips['total_bill'][df_tips['smoker'] == 'Yes'],
       legendgroup='Yes', scalegroup='Yes', name='Yes',
       side='negative', line_color='blue'
   ))
   

   • x=df_tips['day'][df_tips['smoker'] == 'Yes']: Places the day values for smokers on the x-axis.
   • y=df_tips['total_bill'][df_tips['smoker'] == 'Yes']: Plots the corresponding total_bill values on the y-axis.
   • legendgroup='Yes': Groups this trace in the legend as "Yes" (Smokers).
   • scalegroup='Yes': Ensures both violins share the same scale for a meaningful comparison.
   • name='Yes': Label for the trace, shown in the legend.
   • side='negative': Positions the violin plot on the left (negative) side of each day.
   • line_color='blue': Colors the outline of the violin plot in blue.

---

3. Second Violin Trace (Non-Smokers):

   fig.add_trace(go.Violin(
       x=df_tips['day'][df_tips['smoker'] == 'No'],
       y=df_tips['total_bill'][df_tips['smoker'] == 'No'],
       legendgroup='No', scalegroup='Yes', name='No',
       side='positive', line_color='red'
   ))
   

   • x=df_tips['day'][df_tips['smoker'] == 'No']: Places the day values for non-smokers on the x-axis.
   • y=df_tips['total_bill'][df_tips['smoker'] == 'No']: Plots the corresponding total_bill values on the y-axis.
   • legendgroup='No': Groups this trace in the legend as "No" (Non-Smokers).
   • scalegroup='Yes': Shares the same scale as the smokers’ violin plot for consistency.
   • name='No': Label for the trace, shown in the legend.
   • side='positive': Positions the violin plot on the right (positive) side of each day.
   • line_color='red': Colors the outline of the violin plot in red.

---

Key Concepts:

• Split Violin Plot: This technique displays two overlapping distributions (smokers vs. non-smokers) on either side of a central axis, making it easier to compare the data visually.
• Day-Wise Comparison: By plotting each day on the x-axis, the chart helps identify if tipping patterns differ between smokers and non-smokers across different days of the week.
• Color Coding: The blue and red colors help distinguish between smokers and non-smokers, enhancing visual clarity.

---

Insights You Can Gain:

• Behavioral Patterns: Compare tipping behavior between smokers and non-smokers on specific days.
• Distribution Shape: Assess if the distribution of total_bill values differs significantly between the two groups.

This type of plot is useful in exploratory data analysis (EDA) to understand categorical differences within a dataset.

Density Heatmaps

flights = sns.load_dataset("flights")
flights

fig = px.density_heatmap(flights, x='year', y='month', 
                        z='passengers', color_continuous_scale='Viridis')

fig

Explanation of the Code:

This code uses the Seaborn flights dataset and Plotly Express to create a density heatmap showing the number of passengers over different months and years.

---

Step-by-Step Breakdown:

1. Load the Dataset:

   flights = sns.load_dataset("flights")
   

   • sns.load_dataset("flights"): Loads the built-in Seaborn dataset named flights.
     This dataset contains:
     • year: Years from 1949 to 1960.
     • month: Months from January to December.
     • passengers: Number of airline passengers each month (in thousands).

---

2. Create the Heatmap:

   fig = px.density_heatmap(
       flights, x='year', y='month', 
       z='passengers', color_continuous_scale='Viridis'
   )
   

   • px.density_heatmap(): Creates a density heatmap using Plotly Express.
   • flights: The DataFrame used for plotting.
   • x='year': Places the year values on the x-axis.
   • y='month': Places the month values on the y-axis.
   • z='passengers': The color intensity is based on the number of passengers.
   • color_continuous_scale='Viridis': Applies the Viridis color scale to represent the passenger count, where darker colors typically indicate lower values and lighter colors indicate higher values.

---

What the Heatmap Shows:

• Color Intensity: Represents the number of passengers. Brighter (or lighter) colors indicate months/years with more passengers.
• Patterns Over Time: This heatmap makes it easy to observe trends and patterns in passenger numbers across different months and years.
• Seasonal Trends: You might notice specific months (like July or August) where passenger numbers are consistently higher.

---

Use Case:

This type of visualization is useful for identifying trends, seasonal patterns, and anomalies in time-series data, especially when dealing with categorical time variables like months and years.

flights = sns.load_dataset("flights")
flights

fig = px.density_heatmap(flights, x='year', y='month', 
                        z='passengers',
                        marginal_x='histogram',
                        marginal_y='histogram')

fig

Explanation of the Code:

This code creates a density heatmap with marginal histograms along the x-axis (year) and y-axis (month) to provide additional context about the distribution of passenger data.

---

Step-by-Step Breakdown:

1. Load the Dataset:

   flights = sns.load_dataset("flights")
   

   • Loads the flights dataset from Seaborn, containing:
     • year: Years from 1949 to 1960.
     • month: Months from January to December.
     • passengers: Number of airline passengers per month.

---

2. Create the Heatmap with Marginal Histograms:

   fig = px.density_heatmap(
       flights, 
       x='year', 
       y='month', 
       z='passengers',
       marginal_x='histogram',
       marginal_y='histogram'
   )
   

   • px.density_heatmap(): Generates a density heatmap.
   • x='year': Years are plotted on the x-axis.
   • y='month': Months are plotted on the y-axis.
   • z='passengers': The color intensity represents the number of passengers.
   • marginal_x='histogram': Adds a histogram on the x-axis (showing passenger distribution across years).
   • marginal_y='histogram': Adds a histogram on the y-axis (showing passenger distribution across months).

---

What the Visualization Shows:

1. Density Heatmap (Main Plot):

   • Shows the number of passengers by year and month.
   • Color intensity reflects passenger volume, with lighter colors indicating higher values.

2. Marginal Histograms:

   • X-axis Histogram (Top): Summarizes the total passenger count for each year. Helps identify which years had more airline traffic.
   • Y-axis Histogram (Right): Summarizes the total passenger count for each month across all years. Reveals seasonal trends or peak months (e.g., higher passenger numbers during summer).

---

Key Insights:

• Trends Over Time: You can see how passenger numbers increased over the years.
• Seasonality: Identify peak travel months and compare their distribution across different years.
• Marginal Views: The histograms complement the heatmap, providing a quick summary of passenger trends over time and by month.

---

Use Case:

This type of plot is helpful for exploring trends in time-series data and understanding data distributions in more detail. It's widely used in fields like transportation analysis, sales forecasting, and seasonal pattern recognition.

3D Scatter Plots

fig=px.scatter_3d(flights, x='year', y='month', z='passengers',
                 color='year', opacity=0.7)
fig

Explanation of the Code:

This code creates a 3D scatter plot using Plotly, visualizing the flights dataset. The plot represents passenger data over time, with three dimensions: year, month, and the number of passengers.

---

Step-by-Step Breakdown:

1. Create the 3D Scatter Plot:

   fig = px.scatter_3d(
       flights, 
       x='year', 
       y='month', 
       z='passengers', 
       color='year', 
       opacity=0.7
   )
   

   • px.scatter_3d(): Creates a 3D scatter plot.
   • x='year': The year is plotted on the x-axis.
   • y='month': The month is plotted on the y-axis.
   • z='passengers': The number of passengers is plotted on the z-axis.
   • color='year': Colors the points according to the year. Each year will have a different color, making it easy to distinguish the data points by year.
   • opacity=0.7: Sets the opacity of the points, making them partially transparent to better view overlapping points.

---

What the Visualization Shows:

• 3D Scatter Plot:
  • X-axis (Year): The horizontal axis represents the years.
  • Y-axis (Month): The vertical axis represents the months (1 to 12).
  • Z-axis (Passengers): The depth axis represents the number of passengers.
  • Color Encoding (by Year): The color of each point corresponds to the year, with different years having distinct colors. This helps in identifying trends or changes over time.
  • Opacity: The points are made semi-transparent (opacity of 0.7) to reduce overlap and make the plot easier to interpret.

---

Key Insights:

• Temporal Patterns: You can observe how the number of passengers has changed over the years, with patterns that may be influenced by seasonal trends.
• Seasonality: The month axis allows you to see the seasonal distribution of passengers, helping identify if certain months have significantly higher traffic.
• Yearly Comparison: By coloring the points based on year, you can visually compare how passenger numbers evolved across different years.

---

Use Case:

This 3D scatter plot is useful for analyzing multi-dimensional data, especially in cases where you need to explore relationships between multiple variables. It’s great for visualizing trends over time, seasonal effects, or any dataset where three variables interact, like transportation data, sales, or climate data.

3D Line Plots

fig=px.line_3d(flights, x='year', y='month', z='passengers',
                 color='year')
fig

Explanation of the Code:

This code creates a 3D line plot using Plotly, visualizing the flights dataset in a three-dimensional space, where the data points represent passengers over time.

---

Step-by-Step Breakdown:

1. Create the 3D Line Plot:

   fig = px.line_3d(
       flights, 
       x='year', 
       y='month', 
       z='passengers', 
       color='year'
   )
   

   • px.line_3d(): Creates a 3D line plot, allowing for the visualization of data across three dimensions.
   • x='year': The year is represented on the x-axis.
   • y='month': The month is represented on the y-axis.
   • z='passengers': The number of passengers is represented on the z-axis, defining the depth of the plot.
   • color='year': The points along the line are colored based on the year, allowing you to distinguish data for different years.

   This plot will connect the data points with lines, showing trends in passenger numbers over time across different years and months.

---

What the Visualization Shows:

• 3D Line Plot:
  • X-axis (Year): The horizontal axis represents years, displaying the passage of time.
  • Y-axis (Month): The vertical axis represents months, from 1 to 12, showing seasonal trends.
  • Z-axis (Passengers): The depth axis represents the number of passengers, which is the variable of interest.
  • Line and Color Encoding: A line is drawn across the points, showing how the number of passengers changes over both months and years. The color of the line represents different years, making it easy to identify how passenger numbers varied across different years.

---

Key Insights:

• Trends Over Time: The line plot will show how passenger numbers have changed over the years. You can visually detect any trends, such as growth or decline, as well as seasonal variations in the number of passengers.
• Yearly Comparison: By coloring the lines based on year, you can easily compare how passenger trends evolve year-over-year, helping identify any significant changes or patterns.
• Seasonality: The month axis allows you to observe if there is any periodicity or seasonality in passenger numbers, with peaks in certain months or years.

---

Use Case:

This 3D line plot is particularly useful for visualizing how three variables interact over time. It is beneficial for exploring complex datasets that involve time series data across multiple dimensions (e.g., passengers, sales, temperature, etc.). This type of plot can help to uncover trends, periodicity, and relationships between multiple variables over time.

Scatter Matrix

fig = px.scatter_matrix(flights, color ='month')
fig

Explanation of the Code:

This code creates a scatter matrix plot using Plotly, which is a grid of scatter plots that allow you to visualize pairwise relationships between multiple variables in a dataset.

---

Step-by-Step Breakdown:

1. Create the Scatter Matrix Plot:

   fig = px.scatter_matrix(flights, color='month')
   

   • px.scatter_matrix(): This function creates a scatter matrix plot. A scatter matrix (or pair plot) visualizes the relationships between multiple numerical variables by displaying scatter plots for each pair of variables in the dataset.
   • flights: This is the dataset used for the plot. It contains information about flights, including columns like year, month, and passengers.
   • color='month': The data points in each scatter plot are colored based on the month column. This allows you to differentiate data points by month in the matrix.

---

What the Visualization Shows:

• Pairwise Scatter Plots: The scatter matrix shows scatter plots for every pair of numerical variables (columns) in the dataset. For example, in the flights dataset, the scatter matrix would show pairwise relationships like:
  • year vs. month
  • year vs. passengers
  • month vs. passengers
• Coloring by Month: The points in each scatter plot are colored according to the month value. This means that each month will have a distinct color, allowing you to visually assess any patterns or clusters related to specific months.
• Diagonal Subplots: The diagonal part of the matrix typically shows the distribution of each variable, like a histogram or density plot, to understand the distribution of individual columns.

---

What Insights You Can Get from the Scatter Matrix Plot:

1. Relationships Between Variables: By looking at the scatter plots, you can understand how pairs of variables are related. For example, you can assess whether the number of passengers increases over the years or if certain months consistently have higher passenger numbers.
2. Clusters or Groupings: If the data points are grouped in clusters in the scatter plots, it may indicate certain patterns or trends within specific months or years.
3. Outliers: Outliers in the data can be easily spotted as points that fall far from the general trend in the scatter plots.
4. Distribution of Variables: The diagonal histograms help you see the distribution of individual variables (like how passenger numbers vary across different months and years).

---

Use Case:

A scatter matrix plot is ideal for exploring relationships in datasets with multiple numerical variables. In this case, it’s used to explore the relationships between year, month, and passenger counts in the flights dataset. This kind of visualization is helpful when you want to explore how different variables interact and whether there are any distinct patterns or correlations across them.

Maps Scatter Plots

df = px.data.gapminder().query("year == 2007")
fig = px.scatter_geo(df, locations = "iso_alpha",
                    color="continent",
                    hover_name="country",
                    size='pop',
                    projection='orthographic')
fig

Explanation of the Code:

This code creates a geospatial scatter plot using Plotly, specifically a scatter_geo plot. It visualizes the data for the year 2007 on a world map, with points representing countries, colored by their continent, and sized based on population.

---

Step-by-Step Breakdown:

1. Filter the Data for the Year 2007:

   df = px.data.gapminder().query("year == 2007")
   

   • px.data.gapminder(): This loads the Gapminder dataset, which contains data on various countries over different years, including variables like GDP per capita, life expectancy, population, and continent.
   • .query("year == 2007"): This filters the dataset to include only the data for the year 2007. We are focusing on that year for visualization.

2. Create the Scatter Geo Plot:

   fig = px.scatter_geo(df, locations="iso_alpha",
                        color="continent",
                        hover_name="country",
                        size='pop',
                        projection='orthographic')
   

   • px.scatter_geo(): This function is used to create a scatter plot on a geographical map. It places points (representing countries) on a world map based on their geographical location.

3. Parameters in the Scatter Geo Plot:

   • locations="iso_alpha": This parameter specifies that the location of each point (country) is determined by its ISO Alpha-3 country code. The iso_alpha column in the Gapminder dataset provides these country codes.
   • color="continent": This colors the points based on the continent each country belongs to. Each continent will be assigned a different color, helping to visually differentiate regions.
   • hover_name="country": When you hover over each point on the map, it displays the country name.
   • size='pop': The size of each point is determined by the country's population (pop), making larger populations appear as larger points.
   • projection='orthographic': This sets the type of map projection used for the plot. The orthographic projection creates a globe-like view, where the map looks like it’s seen from space.

---

What the Visualization Shows:

• Geographical Representation: Each point represents a country, placed on the map according to its geographical location (latitude and longitude).
• Continent Coloring: Countries are colored based on their continent. For example, countries in Africa may be one color, those in Asia another, etc.
• Population Size: The size of each point reflects the population of the country. Countries with larger populations will have larger circles.
• Hover Information: When you hover over a point on the map, you’ll see the name of the country displayed, making it easy to identify each country on the map.
• Orthographic Projection: The map will have an "earth-like" appearance, as if you're viewing the globe from space, which helps give a sense of the world’s geography in a 3D view.

---

Use Case:

This type of visualization is useful for showing how countries are distributed around the world, as well as how their population sizes and continental locations vary. The scatter plot gives insight into the global spread of populations across different continents and helps to identify any geographical patterns or trends.

from urllib.request import urlopen
import json

# Open the URL and load the JSON data
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv',
                dtype={"fips": str})

fig = px.choropleth(df, geojson= counties, locations='fips', color='unemp',
                    color_continuous_scale='Viridis',
                    range_color=(0, 12),
                    scope='usa',
                    labels={'unemp' : 'Unemployment Rate'})

fig

This code creates a choropleth map using Plotly Express to visualize unemployment rates across U.S. counties. Here's a step-by-step explanation:

1. Import Libraries

from urllib.request import urlopen
import json

• urlopen: This function from the urllib.request module is used to open URLs and retrieve data from the web.
• json: This module is used to parse JSON data, which is a popular format for exchanging data between a server and a web application.

2. Load GeoJSON Data

with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)

• urlopen opens the specified URL (https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json), which contains GeoJSON data for U.S. counties.
• json.load(response) loads the content of the URL (which is a JSON object) into the variable counties. This data includes geographic boundaries for each U.S. county.

3. Load Unemployment Data

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv', dtype={"fips": str})

• pd.read_csv reads the CSV file from the provided URL (https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv), which contains unemployment data for each U.S. county.
• dtype={"fips": str} specifies that the fips column (which contains county codes) should be treated as strings, as it consists of numerical values that should not be interpreted as numbers (e.g., to preserve leading zeros).

4. Create Choropleth Map

fig = px.choropleth(df, geojson= counties, locations='fips', color='unemp',
                    color_continuous_scale='Viridis',
                    range_color=(0, 12),
                    scope='usa',
                    labels={'unemp' : 'Unemployment Rate'})

• px.choropleth: This function from Plotly Express creates a choropleth map.
  • df: The dataframe that contains the data to be visualized, including the county-level unemployment data.
  • geojson= counties: The GeoJSON data for the counties is passed here, which provides the geographic boundaries.
  • locations='fips': The fips column in the df dataframe is used to match the locations to the GeoJSON data, linking the unemployment data to each county's geographic boundary.
  • color='unemp': The color of each county on the map is based on the unemp column, which represents the unemployment rate for that county.
  • color_continuous_scale='Viridis': The color scale for the map is set to "Viridis", a perceptually uniform color scale, which helps in better distinguishing between different levels of unemployment.
  • range_color=(0, 12): This specifies the range of unemployment rates that the map will display. Counties with unemployment rates between 0% and 12% will be color-coded.
  • scope='usa': The scope of the map is limited to the United States.
  • labels={'unemp' : 'Unemployment Rate'}: This sets a custom label for the color scale, renaming the default unemp label to "Unemployment Rate".

5. Display the Figure

fig

• This command displays the choropleth map in the notebook or interface. The map visualizes unemployment rates for each U.S. county based on the data provided.

---

Summary:

The code uses Plotly Express to create a choropleth map showing unemployment rates for U.S. counties in 2016. The map is based on two datasets:

• A GeoJSON file for county boundaries (geojson-counties-fips.json).
• A CSV file containing unemployment data for each county (fips-unemp-16.csv).

The unemployment data is represented by color intensity on the map, with a color scale from 0% to 12% unemployment. The "Viridis" color scale is applied to represent this data, and the map is focused on the U.S.

Polar Charts

df_wind = px.data.wind()
px.scatter_polar(df_wind, r='frequency', theta='direction',
                color='strength', size ='frequency', symbol='strength')

This code creates a polar scatter plot using Plotly Express. Here's an explanation of each part of the code:

1. Load the Wind Dataset

df_wind = px.data.wind()

• px.data.wind(): This loads a built-in dataset from Plotly Express called wind. The dataset contains information about wind directions, the frequency of occurrences of those directions, and the strength of the wind. It typically has the following columns:
  • direction: The direction of the wind (e.g., North, South, etc.)
  • frequency: The number of times that wind direction was observed
  • strength: A measure of the wind's strength (e.g., light, medium, strong)

2. Create the Polar Scatter Plot

px.scatter_polar(df_wind, r='frequency', theta='direction',
                color='strength', size='frequency', symbol='strength')

• px.scatter_polar(): This function creates a polar scatter plot. Polar coordinates (r, theta) are used in this plot, where:

  • r is the radial axis (distance from the center).
  • theta is the angular axis (angle around the center).

  The parameters in this function control how the data is represented in the polar plot.

  • r='frequency': This sets the radial distance (r) of each point to the frequency of the wind in that direction. Points with higher frequency will appear farther from the center of the plot.

  • theta='direction': This sets the angular coordinate (theta) to be the direction of the wind. The wind's direction (e.g., North, East, etc.) will determine the position of each point along the circular axis.

  • color='strength': The strength of the wind is used to color the points. Each point will be colored according to its wind strength (e.g., lighter or darker shades based on wind strength categories like "light," "moderate," or "strong").

  • size='frequency': The frequency of the wind in each direction is also used to determine the size of the points. Higher frequencies will result in larger points, visually indicating that a particular direction occurs more often.

  • symbol='strength': The strength is also used to assign different symbols to the points. For example, strong winds might be represented with a star symbol, while moderate winds could have a different shape.

Summary:

The code creates a polar scatter plot to represent wind data. Each point's position is determined by the wind's direction (theta) and its frequency (r). The color and size of the points represent the strength and frequency of the wind, respectively, while the symbol also indicates the wind's strength.

This plot helps visualize wind direction, frequency, and strength in a compact, circular format, making it easier to see patterns in the wind data.

df_wind = px.data.wind()
px.line_polar(df_wind, r='frequency', theta='direction',
                color='strength', line_close=True, template='plotly_dark')

This code creates a polar line plot to visualize wind data using Plotly Express. Here’s an explanation of each part of the code:

1. Loading the Wind Dataset

df_wind = px.data.wind()

• px.data.wind(): This function loads a built-in dataset in Plotly called wind. It contains data on wind directions, the frequency of occurrences of those directions, and the strength of the wind for each direction.

2. Creating a Polar Line Plot

px.line_polar(df_wind, r='frequency', theta='direction',
                color='strength', line_close=True, template='plotly_dark')

• px.line_polar(): This function creates a polar line plot, which is used to visualize data on a circular scale. The key parameters for this plot are:

  • r='frequency': The r value is used for the radial axis (distance from the center). In this case, frequency represents the frequency of wind occurrences at different directions. The higher the frequency, the further from the center the point will be plotted.

  • theta='direction': The theta value is used for the angular axis (position around the circle). The direction column represents the wind direction (e.g., North, East, etc.). The points will be distributed around the circle based on the direction.

  • color='strength': This parameter controls the color of the lines in the plot. The strength column, which measures the strength of the wind, is used to color the lines. Each line segment will be colored based on the wind's strength, allowing you to visually identify areas of strong and weak winds.

  • line_close=True: This option closes the plot by connecting the last point back to the first point, creating a closed loop around the circle. This is important for circular plots, as it helps form a continuous representation of the data.

  • template='plotly_dark': The template option sets the overall visual theme for the plot. Here, plotly_dark applies a dark theme, where the background is dark, and the plot elements are lighter for better contrast and visibility.

Summary:

This code creates a polar line plot to visualize the wind data, where:

• The angular axis (theta) represents the wind direction (e.g., North, East).
• The radial axis (r) represents the frequency of the wind from each direction.
• The color of the lines is based on the wind strength.
• The plot is closed to form a continuous loop, representing the full cycle of wind directions.
• The plot uses the dark theme (plotly_dark) for better aesthetics.

Use Case:

This type of plot is useful for analyzing and visualizing wind patterns, especially how wind direction and frequency change throughout a given period and how wind strength influences those patterns. The color coding for strength helps to easily identify stronger winds in particular directions.

Ternary Plots

df_exp = px.data.experiment()
px.scatter_ternary(df_exp, a='experiment_1',
                  b='experiment_2', c='experiment_3',
                  hover_name="group", color='gender' )

This code creates a ternary scatter plot using Plotly Express. Let’s break it down to understand what each part does:

1. Loading the Experiment Dataset

df_exp = px.data.experiment()

• px.data.experiment(): This loads a built-in dataset in Plotly called experiment, which contains data from a scientific experiment. The dataset typically includes columns representing different experimental variables (e.g., experiment_1, experiment_2, experiment_3), group labels (e.g., group), and a categorical variable such as gender.

2. Creating a Ternary Scatter Plot

px.scatter_ternary(df_exp, a='experiment_1',
                  b='experiment_2', c='experiment_3',
                  hover_name="group", color='gender')

• px.scatter_ternary(): This function creates a ternary plot, which is a type of plot used to visualize the relationships between three variables that sum up to a constant (often 100%). Ternary plots are useful in fields like chemistry, physics, and biology where proportions of three components are analyzed together.

Key parameters:

• a='experiment_1': This specifies the variable for the first axis of the ternary plot. experiment_1 is one of the columns in the dataset representing a component of the experiment.

• b='experiment_2': This specifies the variable for the second axis of the ternary plot. experiment_2 is another component of the experiment.

• c='experiment_3': This specifies the variable for the third axis of the ternary plot. experiment_3 is the third component.

• hover_name="group": This sets the label that will be shown when hovering over a data point in the plot. In this case, the group column will appear, which likely represents the different groups in the experiment.

• color='gender': This parameter colors the points in the ternary plot based on the gender column in the dataset. Different colors will be applied to points depending on the gender value, allowing for visual differentiation.

What the plot shows:

• The ternary plot will display points where each point represents a data observation, with its position determined by the proportions of experiment_1, experiment_2, and experiment_3 (i.e., the three variables a, b, and c).
• Hovering over each point will show the group it belongs to (due to the hover_name="group").
• Colors will differentiate the data points based on the gender variable.

Use Case:

Ternary plots are useful for visualizing proportions of three components, and this plot specifically helps explore how different experimental variables (experiment_1, experiment_2, experiment_3) relate to one another, while also distinguishing data by gender. The hover functionality enables users to identify the corresponding group for each data point.

This kind of plot can be useful in experiments where the relationship between three variables is central, such as understanding how certain proportions of substances affect an outcome, or how various factors in an experiment interact.

Facet Plots

df_tips = px.data.tips()
px.scatter(df_tips, x='total_bill', y = 'tip', color='smoker',
          facet_col='sex')

This code creates a scatter plot using Plotly Express to visualize the relationship between the total bill and tip in a dataset (df_tips). The dataset contains information about restaurant tips, including whether the customer is a smoker and the gender of the customer. Here’s a breakdown of what the code does:

1. Loading the Dataset

df_tips = px.data.tips()

• px.data.tips(): This loads a built-in dataset in Plotly called tips. The dataset contains data from a restaurant, and it includes columns like:
  • total_bill: The total bill amount for a customer.
  • tip: The tip given by the customer.
  • smoker: A categorical variable indicating whether the customer is a smoker (yes/no).
  • sex: The gender of the customer (male/female).
  • day: The day of the week when the customer dined (e.g., Thursday, Friday, Saturday, Sunday).
  • time: The time of day (Lunch or Dinner).
  • size: The number of people in the dining party.

2. Creating the Scatter Plot

px.scatter(df_tips, x='total_bill', y = 'tip', color='smoker',
           facet_col='sex')

• px.scatter(): This function creates a scatter plot, where the x-axis and y-axis are defined by the variables you specify.

Key parameters:

• x='total_bill': This sets the x-axis to represent the total bill (amount paid by the customer).

• y='tip': This sets the y-axis to represent the tip (amount left as a tip).

• color='smoker': This colors the data points based on whether the customer is a smoker or not (the smoker column). This will visually distinguish points for smokers versus non-smokers using different colors.

• facet_col='sex': This splits the scatter plot into separate subplots (facets) based on the sex of the customer (male or female). So, the plot will create two columns of scatter plots — one for male customers and one for female customers. This allows you to compare the relationships between total bill and tip for different genders.

What the plot shows:

• Scatter Plot: Each point on the plot represents a single customer, with the position of the point determined by the total bill (total_bill) and tip (tip).
• Color Differentiation: The points will be colored based on whether the customer is a smoker or not, allowing you to visually compare the smoking status.
• Facets: The scatter plot is divided into two facets — one for male customers and one for female customers. This makes it easy to compare the patterns of tips and total bills between the two genders.

Summary:

• The scatter plot visualizes the relationship between total bill and tip for customers in the dataset.
• It uses color to distinguish between smokers and non-smokers.
• The plot is split into two subplots (facets) based on the gender of the customers, allowing for a clear comparison between male and female customers.

This is a good way to explore how smoking status and gender might affect the tip behavior in relation to the total bill amount.

px.histogram(df_tips, x='total_bill', y ='tip', color='sex',
            facet_row='time', facet_col='day',
            category_orders={'day': ['Thur', 'Fri', 'Sat', 'Sun'],
                            'time': ['Lunch', 'Dinner']})

This code creates a histogram using Plotly Express to visualize the distribution of the total bill and tip in the df_tips dataset, with additional features like facetting and color coding. Here's a breakdown of the code:

1. Loading the Dataset

df_tips = px.data.tips()

• px.data.tips(): This loads the built-in tips dataset, which includes the following columns:
  • total_bill: The total amount of the bill for a customer.
  • tip: The tip given by the customer.
  • sex: Gender of the customer (Male/Female).
  • smoker: Whether the customer is a smoker (Yes/No).
  • day: The day of the week (Thursday, Friday, Saturday, Sunday).
  • time: The time of day (Lunch or Dinner).
  • size: The number of people in the dining party.

2. Creating the Histogram

px.histogram(df_tips, x='total_bill', y='tip', color='sex',
             facet_row='time', facet_col='day',
             category_orders={'day': ['Thur', 'Fri', 'Sat', 'Sun'],
                              'time': ['Lunch', 'Dinner']})

• px.histogram(): This function creates a histogram based on the dataset df_tips.

Key parameters:

• x='total_bill': This sets the x-axis to represent the total bill.

• y='tip': This sets the y-axis to represent the tip. In a typical histogram, you would usually have just one axis, but since this is a 2D histogram (a bar plot), both the total bill and tip values are used in the plot.

• color='sex': This adds a color differentiation to the histogram based on the sex of the customer. Male and female customers will be colored differently in the histogram bars, making it easier to compare their distributions.

• facet_row='time': This creates subplots (facets) in rows based on the time of day (either Lunch or Dinner). The plots will be arranged in two rows, one for lunch and one for dinner.

• facet_col='day': This creates subplots (facets) in columns based on the day of the week. The plots will be arranged in columns for the days of the week (Thursday, Friday, Saturday, Sunday).

• category_orders: This specifies the custom ordering for categorical variables:

  • 'day': ['Thur', 'Fri', 'Sat', 'Sun']: This orders the days of the week in a specific sequence, from Thursday to Sunday.
  • 'time': ['Lunch', 'Dinner']: This orders the time of day (Lunch first, then Dinner).

What the plot shows:

• Histograms: The plot will create a series of histograms for different combinations of day (Thursday to Sunday) and time (Lunch and Dinner).

  • Each histogram will display the distribution of total bill and tip for that combination of day and time.

• Color Differentiation: The histograms will show the distribution of tips and total bills for Male and Female customers using different colors.

• Facets: The plot is divided into multiple subplots:

  • Rows represent different meal times (Lunch or Dinner).
  • Columns represent the days of the week (Thursday, Friday, Saturday, Sunday).

Summary:

• The code visualizes the distribution of total bill and tip amounts for customers, segmented by gender (Male/Female).
• It creates separate histograms for each meal time (Lunch/Dinner) and day of the week (Thursday, Friday, Saturday, Sunday) using facets.
• The color parameter differentiates between Male and Female customers in the histograms, and the category orders ensure that the days and times are shown in a logical sequence.

This approach helps in analyzing how tipping behavior (total bill and tip) differs based on the day of the week, meal time, and customer gender.

att_df=sns.load_dataset('attention')
fig = px.line(att_df, x='solutions', y = 'score',
             facet_col='subject',
             facet_col_wrap=5,
             title='Scores Based on Attention')
fig

This code visualizes the Scores Based on Attention from the attention dataset, which is loaded from Seaborn, using Plotly Express to create a line plot. Here's an explanation of each part of the code:

1. Loading the Dataset

att_df = sns.load_dataset('attention')

• sns.load_dataset('attention'): This loads the attention dataset from Seaborn. This dataset contains information about participants' scores based on different solutions and subjects. The columns in the dataset are:
  • solutions: The number of solutions or exercises attempted by the participants.
  • score: The score obtained by the participants based on their attention and performance.
  • subject: The subject area of the test (such as 'Math', 'History', etc.).

2. Creating the Line Plot

fig = px.line(att_df, x='solutions', y='score',
             facet_col='subject',
             facet_col_wrap=5,
             title='Scores Based on Attention')

• px.line(): This function creates a line plot using the attention dataset.

Key parameters:

• x='solutions': The x-axis represents the number of solutions attempted by participants. It shows how the solutions are distributed across different scores.

• y='score': The y-axis represents the score achieved by the participants for each number of solutions attempted.

• facet_col='subject': This parameter creates faceted subplots for each unique value in the subject column (such as 'Math', 'History', etc.). It allows you to visualize how the relationship between solutions and score varies across different subjects.

• facet_col_wrap=5: This parameter wraps the facets into a maximum of 5 columns, meaning that if there are more subjects than 5, the facets will be displayed in multiple rows, with each row containing up to 5 subjects.

• title='Scores Based on Attention': This sets the title of the overall plot.

3. Resulting Plot

• Line Plot: The plot shows a line graph for each subject (Math, History, etc.), displaying how the score changes based on the number of solutions attempted. Each line in the facet corresponds to a subject.

• Facets: The data is divided into different subplots based on the subject. There will be one subplot for each unique subject, and each subplot will have the same axes (solutions on the x-axis and score on the y-axis), but the line plots will differ depending on the subject.

• Facet Wrapping: Since facet_col_wrap=5 is specified, if there are more than 5 unique subjects, the subjects will be displayed in multiple rows, with 5 subjects per row. This makes the plot more readable by preventing an excessive horizontal layout.

Summary:

• The code visualizes how the score changes based on the number of solutions for different subjects.
• The plot is faceted by subject, and each facet (subplot) shows a line plot of solutions vs. score for one subject.
• The plot is well-organized by wrapping facets in rows with a maximum of 5 columns, ensuring clarity and better organization of the subject-based plots.

Animated Plots

df_cnt = px.data.gapminder()
px.scatter(df_cnt, x='gdpPercap', y='lifeExp',
          animation_frame='year',
          animation_group='country',
          size='pop', color='continent',
          hover_name='country',
          log_x=True, size_max=55, range_x=[100, 100000],
          range_y = [25, 90])

This code creates an animated scatter plot using the Gapminder dataset, visualizing the relationship between GDP per capita (gdpPercap) and life expectancy (lifeExp) over time, with several additional features to enhance the visualization. Here's an explanation of each part of the code:

1. Loading the Dataset

df_cnt = px.data.gapminder()

• px.data.gapminder(): This loads the Gapminder dataset from Plotly Express. The dataset contains information about different countries across multiple years, including data on:
  • gdpPercap: GDP per capita.
  • lifeExp: Life expectancy.
  • pop: Population.
  • continent: Continent of the country.
  • country: The name of the country.
  • year: The year the data was recorded.

2. Creating the Animated Scatter Plot

px.scatter(df_cnt, x='gdpPercap', y='lifeExp',
          animation_frame='year',
          animation_group='country',
          size='pop', color='continent',
          hover_name='country',
          log_x=True, size_max=55, range_x=[100, 100000],
          range_y = [25, 90])

• px.scatter(): This function creates a scatter plot using Plotly Express.

Key parameters:

• x='gdpPercap': The x-axis represents GDP per capita (gdpPercap), showing the economic output of each country divided by its population.

• y='lifeExp': The y-axis represents life expectancy (lifeExp), showing the average life expectancy in each country.

• animation_frame='year': This creates an animation that progresses over the years (year column). The scatter plot updates for each year, showing how GDP per capita and life expectancy evolve over time.

• animation_group='country': This ensures that the same country is represented across all frames in the animation. The data points for each country will be connected, so you can track the trajectory of each country over time.

• size='pop': The size of each point in the scatter plot represents the population (pop) of the country. Larger points correspond to countries with higher populations.

• color='continent': The color of each point corresponds to the continent (continent column) to which the country belongs. This allows you to visually distinguish countries by their geographic region.

• hover_name='country': When you hover over a data point, the country name will appear as part of the hover information.

• log_x=True: This sets the x-axis to a logarithmic scale, which is useful for visualizing large differences in GDP per capita, especially since some countries have much higher GDPs than others. A log scale compresses large values and helps to spread out the data points more evenly.

• size_max=55: This sets the maximum size of the bubbles (representing population) to 55. Larger populations will have larger bubbles, but the size of the largest bubble will be capped at 55.

• range_x=[100, 100000]: This sets the range of the x-axis to be between 100 and 100,000, ensuring that the x-axis covers a reasonable range of GDP per capita values.

• range_y=[25, 90]: This sets the range of the y-axis to be between 25 and 90, focusing on the typical range of life expectancy values in the dataset.

3. Resulting Plot

• Animated Scatter Plot: The plot will animate over the years, showing how countries' GDP per capita and life expectancy change. Each country is represented by a point in the plot, with:

  • The x-coordinate representing GDP per capita.
  • The y-coordinate representing life expectancy.
  • The size of each point representing the population of the country.
  • The color of each point corresponding to the continent.

• Logarithmic x-axis: The x-axis is on a logarithmic scale, which helps to better visualize the disparities between countries with very high and very low GDP per capita.

• Faceted Animation: The animation progresses year by year, allowing you to see how countries' GDP and life expectancy evolve over time.

• Hover Information: When you hover over a point, it shows the country name, allowing you to identify specific countries in the animation.

Summary:

The code visualizes the evolution of GDP per capita vs life expectancy for countries across multiple years, using an animated scatter plot. The size of the points represents the population, and the color represents the continent. The x-axis is logarithmic to better display differences in GDP, and the plot includes interactive hover information for each country.

px.bar(df_cnt, x='continent', y = 'pop', color='continent', 
      animation_frame='year', animation_group='country',
      range_y=[0, 4000000000])

The code you provided creates an animated bar chart using Plotly Express with the Gapminder dataset (df_cnt) that shows the population (pop) by continent for each year. Here's a breakdown of what each part of the code does:

Code Breakdown:

px.bar(df_cnt, x='continent', y='pop', color='continent', 
      animation_frame='year', animation_group='country',
      range_y=[0, 4000000000])

1. px.bar()

• px.bar() is the Plotly Express function for creating bar charts. This function takes in a dataset and various parameters to configure the chart.

2. x='continent'

• x='continent': The x-axis represents the continent (continent column). Each bar in the chart corresponds to one of the continents, and the bars will be grouped by continent.

3. y='pop'

• y='pop': The y-axis represents the population (pop column) for each continent. The height of each bar will represent the total population for that continent in a given year.

4. color='continent'

• color='continent': This parameter assigns different colors to the bars based on the continent. Each continent will have a distinct color, making it easy to visually distinguish the bars for each continent.

5. animation_frame='year'

• animation_frame='year': This enables animation in the chart. The chart will show how the population of each continent changes over time, updating for each year (year column) in the dataset. The animation will run through each year sequentially, allowing you to track population changes over time.

6. animation_group='country'

• animation_group='country': This parameter ensures that the same country is grouped across the animation frames. Even though the chart animates over years, the total population of each continent will be accurately calculated for each year by aggregating the populations of individual countries within that continent.

7. range_y=[0, 4000000000]

• range_y=[0, 4000000000]: This sets the y-axis range to go from 0 to 4 billion. The population values for the continents are large, so this range ensures the chart has enough space to accommodate the data without scaling issues. This range can be adjusted based on the actual maximum population value in the dataset.

What this Code Does:

• Creates an animated bar chart showing the population of each continent across different years.
• The bars represent total population by continent for each year, and the animation will display how these populations change over time.
• The bars are color-coded by continent, making it easy to distinguish between them.
• The y-axis shows the population for each continent, with the chart ranging from 0 to 4 billion people.
• The animation will progress frame by frame, displaying the population data for each year, with each year showing the population for each continent.

Summary:

This code visualizes how the population of each continent changes over time, from 1952 to 2007 (the years available in the Gapminder dataset). The animation displays the data by year, and the population values are grouped and color-coded by continent. The bar height indicates the population for each continent, and the y-axis range is set to ensure that the chart can accommodate the largest values.